MAIT (Microbial Abundance In Tumors)

A Kraken2 + Bracken based pipeline for classifying and quantifying microbial reads from GDC TCGA WGS data.

Details

See README_details.md.

Prerequistes

• This pipeline requires a GDC token indicating access to the TCGA WGS read data. Without such a token, the pipeline cannot proceed because the TCGA read data is controlled.

• The pipeline analyzes > 10,000 samples and for practical use must be run on a cluster of computers.

• The complete pipeline uses around 4.5TB of disk space.

• The pipeline has only be tested on a cluster using the slurm scheduler.

Installation

Install and set up Miniforge3

Obtain the project source and create a conda environment with the tools needed to run the project:

unzip MAITv1.0.0.zip
cd MAITv1.0.0
mamba env create -f envs/tcga-wgs-kraken-microbial-quant.yaml
mamba activate tcga-wgs-kraken-microbial-quant

Execution

Set your GDC controlled access authentication token in the environment variable GDC_TOKEN

Run the workflow on a cluster:

./scripts/run_snakemake_slurm.sh \
--workflow-profile workflow/profiles/biowulf \
--sbatch-opts="--time=3-00:00:00 --cpus-per-task=28 --mem=10248"

We've provided a SLURM cluster configuration for the NIH HPC cluster, though it is straightforward to create a Snakemake v8+ cluster config for your particular needs.

In principle, the workflow can be adapted to any system for which snakemake can submit jobs, but we have only tested in on slurm.

Output

The raw counts for each sample by each genus will be found in

results/bracken/matrix/tcga_wgs_primary_tumor_genus_count_matrix.tsv

Post-processing

The directory manuscript_code_and_data contains code for post-processing the tcga_wgs_primary_tumor_genus_count_matrix.tsv and producing tables of raw counts and CPM data. See README.md file in the subdirectory for instructions.

References

1. Lu et al. Metagenome analysis using the Kraken software suite. Nat Protoc. 2022 Dec;17(12):2815-2839. doi: 10.1038/s41596-022-00738-y
2. Ge et al. Comprehensive analysis of microbial content in whole-genome sequencing samples from The Cancer Genome Atlas project. bioRxiv 2024.05.24.595788